The Effects of COVID-19 on Unemployment in Washington and the United States

Introduction

This project looks at the effects of COVID-19 on unemployment in Washington and the United States as a whole. This topic is important as the quarantine has had a large impact on the local and national economy.

The data for this project was acquired from the following sources:

1. COVID-19 in USA by Sudalai Raj Kumar
3. Initial Claims applications for Unemployment Insurance - WA by Washington Employment Security Department

Summary Information

We were curious to see which was the most impacted industry based on the unemployment claims made in week 19. We felt this was an important observation to make as identifying the industry that was affected the most can lead to informed decision making when figuring out who should receive the most financial support. The most impacted industry was found to be Educational services.

Next, we wanted to know which industries in King County were the most negatively impacted by the quarantine. We decided to observe this because again, knowing which industries were most negatively affected can help make informed decisions about which sectors need the most financial support. The 5 most negatively impacted industries were: Not disclosed, Educational services, Professional and technical services, Publishing Industries (except internet) and Hospitals.

Summary Table

This aggregate summary table is based on data collected from data source 1. The data in this table is grouped by date, arranged from the most recent date to the earliest date in record. Grouping the data by date allows us to observe the trend of total positive cases in the US every day and we can develop a more holistic understanding of the impact of the virus by also considering the number of people who recovered or are currently hospitalized.

The table includes information of date observed, total positive cases for a given day, total number of people hospitalized/recovered and the total increase in positive cases compared to the previous day.

It can be observed that at the beginning, the number of positive cases grew by a lot every day at a very fast rate. In April the number of cases stabilized at an average of about 30k every day, while in May this number has lessened to about an average of 24k with the most recent data from May 11th marking about an 18k increase in new cases, showing a slowed growth as time goes on.

Date Positive Cases People Hospitalized People Recovered Increase in Positive Cases (Compared to Previous Day)
2020-05-11 1340412 44191 232733 17605
2020-05-10 1322807 44943 216169 21712
2020-05-09 1301095 46637 212534 25179
2020-05-08 1275916 47718 198993 27779
2020-05-07 1248137 49130 195036 27580
2020-05-06 1220557 50227 189910 24952
2020-05-05 1195605 50906 189791 22152
2020-05-04 1173453 50062 187180 21447
2020-05-03 1152006 50312 180152 26287
2020-05-02 1125719 51734 175382 30038
2020-05-01 1095681 52370 164015 33178
2020-04-30 1062503 53793 154991 29346
2020-04-29 1033157 54930 147484 27020
2020-04-28 1006137 54940 139342 25003
2020-04-27 981134 54971 121609 20791
2020-04-26 960343 55050 116801 27033
2020-04-25 933310 56344 112783 35991
2020-04-24 897319 56075 101517 34196
2020-04-23 863123 57889 97121 31749
2020-04-22 831374 58130 93157 28602
2020-04-21 802772 58579 73002 25942
2020-04-20 776830 55677 69636 25184
2020-04-19 751646 55570 67339 27599
2020-04-18 724047 56591 62961 28037
2020-04-17 696010 57808 53644 31881
2020-04-16 664129 58625 48945 31017
2020-04-15 633112 59260 43522 30317
2020-04-14 602795 58719 39347 25920
2020-04-13 576875 55295 35442 24969
2020-04-12 551906 54749 34151 28013
2020-04-11 523893 54757 31631 31045
2020-04-10 492848 51745 29054 34588
2020-04-09 458260 49826 24869 34215
2020-04-08 424045 44063 21141 30171
2020-04-07 393874 42294 18477 30409
2020-04-06 363465 34663 16584 28747
2020-04-05 334718 30905 14542 25966
2020-04-04 308752 29095 12840 33518
2020-04-03 275234 24564 10861 31999
2020-04-02 243235 21833 8586 28058
2020-04-01 215177 19982 7084 25179
2020-03-31 189998 17856 5666 24477
2020-03-30 165521 15574 4560 21224
2020-03-29 144297 13833 4061 19482
2020-03-28 124815 12179 3148 19353
2020-03-27 105462 10789 2422 18673
2020-03-26 86789 7617 97 17316
2020-03-25 69473 4971 147 12294
2020-03-24 57179 3828 0 10166
2020-03-23 47013 2770 0 10679
2020-03-22 36334 2155 0 8967
2020-03-21 27367 1436 0 6533
2020-03-20 20834 1042 0 5771
2020-03-19 15063 617 0 4198
2020-03-18 10865 416 0 2611
2020-03-17 8254 325 0 2123
2020-03-16 6131 0 0 1280
2020-03-15 4842 0 0 1101
2020-03-14 3741 0 0 737
2020-03-13 3004 0 0 862
2020-03-12 2142 0 0 462
2020-03-11 1672 0 0 392
2020-03-10 1280 0 0 267
2020-03-09 1013 0 0 292
2020-03-08 721 0 0 183
2020-03-07 538 0 0 148
2020-03-06 387 0 0 109
2020-03-05 275 0 0 65
2020-03-04 207 0 0 36
2020-03-03 94 0 0 41
2020-03-02 53 0 0 13
2020-03-01 40 0 0 12
2020-02-29 18 0 0 9
2020-02-28 9 0 0 7
2020-02-27 2 0 0 0
2020-02-26 2 0 0 0
2020-02-25 2 0 0 0
2020-02-24 2 0 0 0
2020-02-23 2 0 0 0
2020-02-22 2 0 0 0
2020-02-21 2 0 0 0
2020-02-20 2 0 0 0
2020-02-19 2 0 0 0
2020-02-18 2 0 0 0
2020-02-17 2 0 0 0
2020-02-16 2 0 0 0
2020-02-15 2 0 0 0
2020-02-14 2 0 0 0
2020-02-13 2 0 0 0
2020-02-12 2 0 0 0
2020-02-11 2 0 0 0
2020-02-10 2 0 0 0
2020-02-09 2 0 0 0
2020-02-08 2 0 0 0
2020-02-07 2 0 0 1
2020-02-06 1 0 0 0
2020-02-05 1 0 0 0
2020-02-04 1 0 0 0
2020-02-03 1 0 0 0
2020-02-02 1 0 0 0
2020-02-01 1 0 0 0
2020-01-31 1 0 0 0
2020-01-30 1 0 0 0
2020-01-29 1 0 0 0
2020-01-28 1 0 0 0
2020-01-27 1 0 0 0
2020-01-26 1 0 0 0
2020-01-25 1 0 0 0
2020-01-24 1 0 0 0
2020-01-23 1 0 0 0
2020-01-22 1 0 0 0

Charts

Data Source 1

This is an area plot chart showing the relationship between the increase in positive cases and number of people currently hospitalized against date. This chart was intended to present the total increase in daily positive cases and currently hospitalized cases from Jan. 23 to May 11. Visualizing this information can help us more easily identify the trend of positive cases and currently hospitalized cases measured daily, and allow us to anticipate the potential trends in unemployment.

Data Source 2

This is a line graph showing the average unemployment claims made in the US, and represents the relationship between the number of initial claims filed on a given date. It is important to observe this information because it allows us to see when the COVID-19 outbreak started to impact the unemployment rates across the country, and this information can be used for instnace to prepare against the next wave of the outbreak to better manage the provision of support for those who become unemployed.

Data Source 3

This plot is a plot of initial claims spanning every industry of every county in Washington that occurred on a weekly basis from weeks 1-19 of the pandemic. After week 10 there has been a surge in the number of initial claims made, jumping from 14154 claims in week 10 to 128962 claims in week 11. From weeks 1-10, the trend stayed primarily constant ranging from about 5000-14000, however, after the spike from week 11, the trend has been very sporadic, as intial claims have fluctuated up and down. The use of a scatterplot is used to show if there is a relationship between two numeric variables and to see if there is any possible correlation. To test to see if weeks and initial cases had correlation (that is as one goes through the weeks of the pandemic, it would be apparent that there is a relationship between week and cases), however to this point, it seems as if there is no relationship or a correlation between the two factors.